Publication Type : Conference Proceedings
Publisher : IOP
Source : IOP Conference Series: Materials Science and Engineering
Url : https://iopscience.iop.org/article/10.1088/1757-899X/1012/1/012034/meta
Campus : Amritapuri
School : School of Physical Sciences
Year : 2021
Abstract : This paper aims at the requirement for an interactive learning framework which empowers the successful checking of disorder in a patient. Principal component analysis stands out as an outstanding algorithm to significantly classify the target classes. PCA blends associated characteristics and makes a dissipated showcase of its components well. Scree plot examination gives solidarity of how many principal components are to be retained. Support Vector Machines (SVM ) is a fast and dependable classification algorithm that outperforms other techniques with a limited amount of data. The obtained components will be served to Support Vector Machine for further classification. The pre-dangerous stage will remind the clinical experts to give additional consideration to those patients. The expectation ability is estimated in terms of the confusion matrix. The model developed gives a high and uncompromising accuracy in early detection of different levels of malignancy
Cite this Research Publication : Manju, B. R., V. Athira, and Athul Rajendran. "Efficient multi-level lung cancer prediction model using support vector machine classifier." IOP Conference Series: Materials Science and Engineering. Vol. 1012. No. 1. IOP Publishing,